Resum
We present an improvement of an estimator of causality in financial time series via transfer entropy, which includes the side information that may affect the cause-effect relation in the system, i.e. a conditional information-transfer based causality. We show that for weakly stationary time series the conditional transfer entropy measure is nonnegative and bounded below by the Geweke's measure of Granger causality. We use k-nearest neighbor distances to estimate entropy and approximate the distribution of the estimator with bootstrap techniques. We give examples of the application of the estimator in detecting causal effects in a simulated autoregressive stationary system in three random variables with linear and non-linear couplings; in a system of non stationary variables; and with real financial data.
Idioma original | Anglès nord-americà |
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Pàgines (de-a) | 31-42 |
Nombre de pàgines | 12 |
Revista | CEUR Workshop Proceedings |
Volum | 1774 |
Estat de la publicació | Publicada - 2016 |